from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
%matplotlib notebook
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 1s
KMeans_short: 0h 0m 2s
daal4py_LogisticRegression: 0h 0m 3s
daal4py_KMeans_tall: 0h 0m 7s
Ridge: 0h 0m 10s
LogisticRegression: 0h 0m 19s
KMeans_tall: 0h 0m 20s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 26s
KNeighborsClassifier_kd_tree: 0h 2m 25s
daal4py_KNeighborsClassifier: 0h 2m 27s
xgboost: 0h 5m 5s
lightgbm: 0h 5m 9s
catboost_symmetric: 0h 5m 9s
HistGradientBoostingClassifier: 0h 5m 10s
catboost_lossguide: 0h 5m 34s
KNeighborsClassifier: 0h 38m 11s
total: 1h 10m 47s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.8.0-1033-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.21.0",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.5",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.125 | 0.000 | 6.387 | 0.000 | -1 | 100 | NaN | NaN | 0.433 | 0.000 | 0.289 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 30.787 | 0.000 | 0.000 | 0.031 | -1 | 100 | 0.928 | 0.688 | 1.733 | 0.018 | 17.761 | 0.181 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.203 | 0.026 | 0.000 | 0.203 | -1 | 100 | 1.000 | 0.000 | 0.080 | 0.000 | 2.540 | 0.327 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.123 | 0.000 | 6.517 | 0.000 | -1 | 5 | NaN | NaN | 0.424 | 0.000 | 0.289 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 30.321 | 0.000 | 0.000 | 0.030 | -1 | 5 | 0.811 | 0.688 | 1.727 | 0.006 | 17.554 | 0.065 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.185 | 0.015 | 0.000 | 0.185 | -1 | 5 | 1.000 | 0.000 | 0.079 | 0.000 | 2.327 | 0.194 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.114 | 0.000 | 7.035 | 0.000 | -1 | 1 | NaN | NaN | 0.422 | 0.000 | 0.269 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.418 | 0.135 | 0.000 | 0.022 | -1 | 1 | 0.736 | 0.805 | 1.735 | 0.014 | 12.920 | 0.129 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.163 | 0.013 | 0.000 | 0.163 | -1 | 1 | 0.000 | 1.000 | 0.079 | 0.001 | 2.059 | 0.162 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.119 | 0.000 | 6.722 | 0.000 | 1 | 5 | NaN | NaN | 0.424 | 0.000 | 0.281 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 20.861 | 0.033 | 0.000 | 0.021 | 1 | 5 | 0.811 | 0.944 | 1.792 | 0.014 | 11.643 | 0.090 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.195 | 0.001 | 0.000 | 0.195 | 1 | 5 | 1.000 | 1.000 | 0.079 | 0.000 | 2.464 | 0.018 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.115 | 0.000 | 6.954 | 0.000 | 1 | 1 | NaN | NaN | 0.426 | 0.000 | 0.270 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 11.784 | 0.015 | 0.000 | 0.012 | 1 | 1 | 0.736 | 0.805 | 1.734 | 0.016 | 6.798 | 0.065 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.181 | 0.001 | 0.000 | 0.181 | 1 | 1 | 0.000 | 1.000 | 0.079 | 0.000 | 2.279 | 0.021 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.115 | 0.000 | 6.973 | 0.000 | 1 | 100 | NaN | NaN | 0.425 | 0.000 | 0.270 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 20.907 | 0.037 | 0.000 | 0.021 | 1 | 100 | 0.928 | 0.944 | 1.800 | 0.024 | 11.612 | 0.159 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.194 | 0.001 | 0.000 | 0.194 | 1 | 100 | 1.000 | 1.000 | 0.082 | 0.006 | 2.370 | 0.162 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.302 | 0.000 | -1 | 100 | NaN | NaN | 0.088 | 0.000 | 0.604 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 27.469 | 0.224 | 0.000 | 0.027 | -1 | 100 | 0.979 | 0.973 | 0.270 | 0.006 | 101.872 | 2.242 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.026 | 0.002 | 0.000 | 0.026 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 4.732 | 0.418 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.050 | 0.000 | 0.321 | 0.000 | -1 | 5 | NaN | NaN | 0.088 | 0.000 | 0.566 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 27.633 | 0.396 | 0.000 | 0.028 | -1 | 5 | 0.981 | 0.973 | 0.270 | 0.004 | 102.293 | 1.997 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.003 | 0.000 | 0.028 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 5.277 | 0.559 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.297 | 0.000 | -1 | 1 | NaN | NaN | 0.088 | 0.000 | 0.615 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.047 | 0.158 | 0.000 | 0.020 | -1 | 1 | 0.974 | 0.979 | 0.270 | 0.004 | 74.256 | 1.246 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.002 | 0.000 | 0.020 | -1 | 1 | 0.000 | 1.000 | 0.005 | 0.000 | 4.000 | 0.535 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.048 | 0.000 | 0.335 | 0.000 | 1 | 5 | NaN | NaN | 0.087 | 0.000 | 0.548 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 16.875 | 0.008 | 0.000 | 0.017 | 1 | 5 | 0.981 | 0.982 | 0.317 | 0.004 | 53.257 | 0.688 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.017 | 0.000 | 0.000 | 0.017 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 3.287 | 0.193 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.047 | 0.000 | 0.338 | 0.000 | 1 | 1 | NaN | NaN | 0.087 | 0.000 | 0.543 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 8.959 | 0.037 | 0.000 | 0.009 | 1 | 1 | 0.974 | 0.979 | 0.272 | 0.002 | 32.946 | 0.298 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.013 | 0.000 | 0.000 | 0.013 | 1 | 1 | 0.000 | 1.000 | 0.005 | 0.000 | 2.492 | 0.131 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.048 | 0.000 | 0.335 | 0.000 | 1 | 100 | NaN | NaN | 0.087 | 0.000 | 0.546 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 16.832 | 0.026 | 0.000 | 0.017 | 1 | 100 | 0.979 | 0.982 | 0.326 | 0.005 | 51.587 | 0.729 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.019 | 0.000 | 0.000 | 0.019 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 3.482 | 0.213 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.767 | 0.000 | 0.029 | 0.000 | -1 | 5 | NaN | NaN | 0.643 | 0.000 | 4.306 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.755 | 0.010 | 0.000 | 0.001 | -1 | 5 | 0.981 | 0.972 | 0.529 | 0.016 | 1.427 | 0.047 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 4.967 | 1.809 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.802 | 0.000 | 0.029 | 0.000 | -1 | 100 | NaN | NaN | 0.651 | 0.000 | 4.304 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.604 | 0.033 | 0.000 | 0.003 | -1 | 100 | 0.977 | 0.974 | 0.174 | 0.002 | 14.990 | 0.253 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.007 | 0.000 | 0.000 | 0.007 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 26.432 | 10.513 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.008 | 0.000 | 0.027 | 0.000 | -1 | 1 | NaN | NaN | 0.661 | 0.000 | 4.553 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.432 | 0.009 | 0.000 | 0.000 | -1 | 1 | 0.964 | 0.974 | 0.173 | 0.002 | 2.497 | 0.056 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 9.644 | 4.578 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.755 | 0.000 | 0.029 | 0.000 | 1 | 1 | NaN | NaN | 0.660 | 0.000 | 4.175 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.696 | 0.004 | 0.000 | 0.001 | 1 | 1 | 0.964 | 0.958 | 0.094 | 0.001 | 7.394 | 0.089 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.683 | 3.190 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.824 | 0.000 | 0.028 | 0.000 | 1 | 5 | NaN | NaN | 0.657 | 0.000 | 4.301 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.313 | 0.017 | 0.000 | 0.001 | 1 | 5 | 0.981 | 0.958 | 0.094 | 0.001 | 13.967 | 0.261 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 9.345 | 5.708 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.877 | 0.000 | 0.028 | 0.000 | 1 | 100 | NaN | NaN | 0.653 | 0.000 | 4.403 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.371 | 0.024 | 0.000 | 0.004 | 1 | 100 | 0.977 | 0.972 | 0.525 | 0.006 | 8.324 | 0.110 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.000 | 0.000 | 0.005 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 6.658 | 2.103 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.711 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.435 | 0.000 | 1.635 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.002 | 0.001 | 0.000 | -1 | 5 | 0.982 | 0.979 | 0.006 | 0.001 | 3.825 | 0.438 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 19.659 | 15.969 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.700 | 0.000 | 0.023 | 0.000 | -1 | 100 | NaN | NaN | 0.429 | 0.000 | 1.633 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.038 | 0.000 | 0.000 | 0.000 | -1 | 100 | 0.983 | 0.982 | 0.001 | 0.000 | 35.136 | 9.193 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 21.891 | 18.451 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.700 | 0.000 | 0.023 | 0.000 | -1 | 1 | NaN | NaN | 0.429 | 0.000 | 1.633 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.020 | 0.000 | 0.001 | 0.000 | -1 | 1 | 0.977 | 0.982 | 0.001 | 0.000 | 19.745 | 6.635 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 21.102 | 17.322 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.709 | 0.000 | 0.023 | 0.000 | 1 | 1 | NaN | NaN | 0.430 | 0.000 | 1.650 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.019 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.977 | 0.978 | 0.001 | 0.000 | 25.765 | 9.661 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.273 | 4.452 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.692 | 0.000 | 0.023 | 0.000 | 1 | 5 | NaN | NaN | 0.424 | 0.000 | 1.632 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.021 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.982 | 0.978 | 0.001 | 0.000 | 29.761 | 11.012 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.832 | 5.015 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.696 | 0.000 | 0.023 | 0.000 | 1 | 100 | NaN | NaN | 0.421 | 0.000 | 1.652 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.046 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.983 | 0.979 | 0.006 | 0.001 | 7.699 | 0.878 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.630 | 4.635 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.547 | 0.0 | 0.877 | 0.000 | random | NaN | 30 | NaN | 0.372 | 0.0 | 1.470 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.419 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.994 | 4.803 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.540 | 8.087 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.514 | 0.0 | 0.935 | 0.000 | k-means++ | NaN | 30 | NaN | 0.404 | 0.0 | 1.272 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.413 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.575 | 5.208 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.086 | 7.359 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.035 | 0.0 | 3.977 | 0.000 | random | NaN | 30 | NaN | 2.585 | 0.0 | 2.335 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 14.720 | 0.000 | random | 0.002 | 30 | 0.001 | 0.000 | 0.0 | 6.431 | 3.311 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.021 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.431 | 5.079 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.901 | 0.0 | 4.067 | 0.000 | k-means++ | NaN | 30 | NaN | 2.749 | 0.0 | 2.147 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.001 | 0.0 | 16.660 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.435 | 2.402 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.021 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.474 | 5.047 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.214 | 0.0 | 0.015 | 0.000 | k-means++ | NaN | 20 | NaN | 0.032 | 0.0 | 6.766 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.202 | 0.000 | k-means++ | 0.004 | 20 | -0.001 | 0.001 | 0.0 | 2.573 | 0.485 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.460 | 6.460 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.079 | 0.0 | 0.041 | 0.000 | random | NaN | 20 | NaN | 0.087 | 0.0 | 0.907 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.202 | 0.000 | random | 0.001 | 20 | -0.001 | 0.001 | 0.0 | 2.438 | 0.424 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.419 | 6.689 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.578 | 0.0 | 0.277 | 0.000 | k-means++ | NaN | 20 | NaN | 0.141 | 0.0 | 4.087 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 7.104 | 0.000 | k-means++ | 0.285 | 20 | 0.302 | 0.001 | 0.0 | 2.055 | 0.339 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.897 | 4.516 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.195 | 0.0 | 0.822 | 0.000 | random | NaN | 20 | NaN | 0.340 | 0.0 | 0.572 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.828 | 0.000 | random | 0.340 | 20 | 0.304 | 0.001 | 0.0 | 2.174 | 0.352 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.818 | 4.140 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.159 | 0.0 | [-0.10572992] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.766 | 0.0 | 6.319 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [54.31415648] | 0.000 | NaN | NaN | NaN | NaN | 0.543 | 0.000 | 0.0 | 0.918 | 0.428 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.22834113] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.460 | 0.401 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.721 | 0.0 | [2.88587009] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.729 | 0.0 | 0.988 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [132.75694692] | 0.000 | NaN | NaN | NaN | NaN | 0.260 | 0.003 | 0.0 | 0.587 | 0.106 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [20.53491476] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.001 | 0.0 | 0.159 | 0.104 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.174 | 0.0 | 0.461 | 0.0 | NaN | NaN | NaN | 0.174 | 0.0 | 0.995 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 8.124 | 0.0 | NaN | NaN | 0.091 | 0.016 | 0.0 | 0.603 | 0.008 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.130 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.747 | 0.755 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.384 | 0.0 | 0.578 | 0.0 | NaN | NaN | NaN | 0.221 | 0.0 | 6.260 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 5.158 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.694 | 0.426 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.014 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.666 | 0.701 | See | See |